climate_types_raster2polygon

Code to produce spatial aggregations of Koppen-Geiger climate types. The spatial aggregation are performed from climate type information from grid/raster (tiff) to polygons (shp).

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Climate Types

Raw climate type maps are referenced to the World Geodetic Reference System 1984 (WGS 84) ellipsoid and made available at three resolutions (0.0083°, 0.083°, and 0.5°; approximately 1 km, 10 km, and 50 km at the equator, respectively).

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Codebook

Dataset Columns:

1. id: Represents the id of a given polygon in a shp file.
2. climate_type_short: The abbreviated code for each specific climate type.
3. climate_type_long: The detailed description of each specific climate type.
4. Af - EF: Each of these columns represents a specific climate type. The values within these columns will represent the percentage area corresponding to each climate type for a specific polygon.

Climate Types (Keys and Descriptions):

• Af: Tropical, rainforest
• Am: Tropical, monsoon
• Aw: Tropical, savannah
• BWh: Arid, desert, hot
• BWk: Arid, desert, cold
• BSh: Arid, steppe, hot
• BSk: Arid, steppe, cold
• Csa: Temperate, dry summer, hot summer
• Csb: Temperate, dry summer, warm summer
• Csc: Temperate, dry summer, cold summer
• Cwa: Temperate, dry winter, hot summer
• Cwb: Temperate, dry winter, warm summer
• Cwc: Temperate, dry winter, cold summer
• Cfa: Temperate, no dry season, hot summer
• Cfb: Temperate, no dry season, warm summer
• Cfc: Temperate, no dry season, cold summer
• Dsa: Cold, dry summer, hot summer
• Dsb: Cold, dry summer, warm summer
• Dsc: Cold, dry summer, cold summer
• Dsd: Cold, dry summer, very cold winter
• Dwa: Cold, dry winter, hot summer
• Dwb: Cold, dry winter, warm summer
• Dwc: Cold, dry winter, cold summer
• Dwd: Cold, dry winter, very cold winter
• Dfa: Cold, no dry season, hot summer
• Dfb: Cold, no dry season, warm summer
• Dfc: Cold, no dry season, cold summer
• Dfd: Cold, no dry season, very cold winter
• ET: Polar, tundra
• EF: Polar, frost

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Run

Conda environment

Clone the repository and create a conda environment.

git clone <https://github.com/<user>/repo>
cd <repo>

conda env create -f requirements.yml
conda activate <env_name> #environment name as found in requirements.yml

It is also possible to use mamba.

mamba env create -f requirements.yml
mamba activate <env_name>

Create input and output data placeholders

Follow the README instructions in the data folder to attach data placeholders to the pipeline.

Pipeline

You can run the pipeline steps manually or run the snakemake pipeline described in the Snakefile.

run pipeline steps manually

python src/download_shapefile.py
python src/download_climate_types.py
python src/aggregate_climate_types.py

run snakemake pipeline or run the pipeline:

snakemake --cores 1 

Dockerized Pipeline

Create the folder where you would like to store the output dataset.

mkdir <path>/climate_types_raster2polygon

Pull and Run:

docker pull nsaph/climate_types_raster2polygon
# to generate county aggregations
docker run -v <test_path>:/app/data/output/present -t nsaph/climate_types_raster2polygon
# to generate zcta aggregations
docker run -v <test_path>:/app/data/output/present -t nsaph/climate_types_raster2polygon --cores 1 

If you want to build your own image use

docker build -t <image_name> .

To dev or test do

docker run --entrypoint /bin/bash -v $(pwd)/:/app -it <image> 

For a multiplatform built use

docker buildx build --platform linux/amd64,linux/arm64 -t <user>/<image>:<version> . --push

Remember this step is unnecessary as the built image is availabe under nsaph/climate_types_raster2polygon:latest.